Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning.

arXiv:2610.17709v2 Announce Type: replace-cross Abstract: Imitation learning typically treats every demonstration state as equally important, optimizing a behavior-cloning objective uniformly across the dataset. Yet not all states are created equal: in a small